A lateral connection: Once that the vertical connection is computed, it is combined with the lateral connection, which merges feature maps of the same spatial size on an element-wise addition. The lateral connection input is generated after a 1x1 convolutional layer is applied to the corresponding Bottom-Up feature map. The aim for the 1x1 convolution is to reduce channel dimensions (d) to a fixed value of d=256. The ultimate objective of the lateral connection is to increase the location capabilities of the backbone model.
Thanks to the introduction of FPN, Lin et al. [1]} improved Faster R-CNN AP@0.5 by 9.6 points by defining anchor areas of \({32^{2},64^{2},128^{2},256^{2}}\)  and \({512^{2}}\)  pixels on pyramid levels \({P_{3},P_{4},P_{5},P_{6},P_{7}}\)  respectively and using anchor aspect ratios of \({1:2,1:1,2:1}\) . Where \(P_{i}\)  represents a feature level with resolution \(\frac{1}{2^{i}}\)  of the input image. So if input resolution is 640x640, then \(P_{3}^{in}\)  represents feature level 3 with a resolution of 80x80 (\(\frac{640}{2^{3}}=80\) ) and \(P_{7}\)  will have a resolution of 5x5 (\(\frac{640}{2^{7}}=5\) ). Such inlet feature \(P_{3}^{in}\)  is combined with a top feature using an upsampling (factor x2) to create the final outlet feature prior to a convolution operation for feature processing as follows:
\(P_{3}^{out}=Conv(P_{3}^{in}+Resize(P_{4}^{out}))\) 
